XGBoost in R Programming
XGBoost is a popular machine learning algorithm and it stands for “Extreme Gradient Boosting.” XGBoost is available in various programming languages, including R. An XGBoost is a fast and efficient algorithm. XG Boost works only with numeric variables. and XGBoost is a fast and efficient algorithm and is used by winners of many machine learning competitions. XG Boost works only with numeric variables. It is widely used for both classification and regression tasks.
In this article, we will learn about What is XGBoost? How to use the XGBoost algorithm in R? specifically a dataset from a big mart that stores attributes and various products ad also you will get to know about the features that are important in the XGBoost model.
What is XGBoost?
It is a part of the boosting technique in which the selection of the sample is done more intelligently to classify observations. There are interfaces of XGBoost in C++, R, Python, Julia, Java, and Scala. The core functions in XGBoost are implemented in C++, thus it is easy to share models among different interfaces. Based on the statistics from the CRAN mirror, the package has been downloaded more than 81, 000 times. XgBoost modeling consists of two techniques: Bagging and Boosting.
- Bagging: It is an approach where you can take random data samples, build learning algorithms, and take simple means to find bagging probabilities.
- Boosting: It is an approach where a selection of approaches is made more intelligently i.e. more and more weight is given to classify observations.
How to use XGBoost algorithm in R ?
Parameters used in XGBoost
- eta: It shrinks the feature weights to make the boosting process more conservative. The range is from 0 to 1. It is also knowm as learning rate or Shrinking factor. Low eta value signifies the model is more robust to overfitting.
- gamma: The larger the value of gamma, more conservative the algorithm will be. It’s range is from 0 to infinity.
- max_depth: The maximum depth of a tree can be specified using max_depth parameter.
- Subsample: It is the proportion of rows that the model will randomly select to grow trees.
- colsample_bytree: It is the ratio of variables randomly chosen to build each tree in the model.
The Dataset
A Big Mart dataset consists of 1559 products across 10 stores in different cities. Certain attributes of each product and store have been defined. It consists of 12 features i.e Item_Identifier( is a unique product ID assigned to every distinct item), Item_Weight(includes the weight of the product), Item_Fat_Content(describes whether the product is low fat or not), Item_Visibility(mentions the percentage of the total display area of all products in a store allocated to the particular product), Item_Type(describes the food category to which the item belongs), Item_MRP(Maximum Retail Price (list price) of the product), Outlet_Identifier(unique store ID assigned. It consists of an alphanumeric string of length 6), Outlet_Establishment_Year(mentions the year in which store was established), Outlet_Size(tells the size of the store in terms of ground area covered), Outlet_Location_Type(tells about the size of the city in which the store is located), Outlet_Type(tells whether the outlet is just a grocery store or some sort of supermarket) and Item_Outlet_Sales( sales of the product in the particular store).
R
# Loading data train = fread("Train_UWu5bXk.csv") test = fread("Test_u94Q5KV.csv") # Structure str(train) |
Output:
Performing XGBoost on Dataset
Using XGBoost algorithm on the dataset which includes 12 features with 1559 products across 10 stores in different cities.
R
# Installing Packages install.packages("data.table") install.packages("dplyr") install.packages("ggplot2") install.packages("caret") install.packages("xgboost") install.packages("e1071") install.packages("cowplot") # Loading packages library(data.table) # for reading and manipulation of data library(dplyr) # for data manipulation and joining library(ggplot2) # for plotting library(caret) # for modeling library(xgboost) # for building XGBoost model library(e1071) # for skewness library(cowplot) # for combining multiple plots # Setting test dataset # Combining datasets # add Item_Outlet_Sales to test data test[, Item_Outlet_Sales := NA] combi = rbind(train, test) # Missing Value Treatment missing_index = which(is.na(combi$Item_Weight)) for(i in missing_index){ item = combi$Item_Identifier[i] combi$Item_Weight[i] = mean(combi$Item_Weight [combi$Item_Identifier == item], na.rm = T) } # Replacing 0 in Item_Visibility with mean zero_index = which(combi$Item_Visibility == 0) for(i in zero_index){ item = combi$Item_Identifier[i] combi$Item_Visibility[i] = mean( combi$Item_Visibility[combi$Item_Identifier == item], na.rm = T) } # Label Encoding # To convert categorical in numerical combi[, Outlet_Size_num := ifelse(Outlet_Size == "Small", 0, ifelse(Outlet_Size == "Medium", 1, 2))] combi[, Outlet_Location_Type_num := ifelse(Outlet_Location_Type == "Tier 3", 0, ifelse(Outlet_Location_Type == "Tier 2", 1, 2))] combi[, c("Outlet_Size", "Outlet_Location_Type") := NULL] # One Hot Encoding # To convert categorical in numerical ohe_1 = dummyVars("~.", data = combi[, -c("Item_Identifier", "Outlet_Establishment_Year", "Item_Type")], fullRank = T) ohe_df = data.table(predict(ohe_1, combi[, -c("Item_Identifier", "Outlet_Establishment_Year", "Item_Type")])) combi = cbind(combi[, "Item_Identifier"], ohe_df) # Remove skewness skewness(combi$Item_Visibility) skewness(combi$price_per_unit_wt) # log + 1 to avoid division by zero combi[, Item_Visibility := log(Item_Visibility + 1)] # Scaling and Centering data # index of numeric features num_vars = which(sapply(combi, is.numeric)) num_vars_names = names(num_vars) combi_numeric = combi[, setdiff(num_vars_names, "Item_Outlet_Sales"), with = F] prep_num = preProcess(combi_numeric, method = c("center", "scale")) combi_numeric_norm = predict(prep_num, combi_numeric) # removing numeric independent variables combi[, setdiff(num_vars_names, "Item_Outlet_Sales") := NULL] combi = cbind(combi, combi_numeric_norm) # Splitting data back to train and test train = combi[1:nrow(train)] test = combi[(nrow(train) + 1):nrow(combi)] # Removing Item_Outlet_Sales test[, Item_Outlet_Sales := NULL] # Model Building: XGBoost param_list = list( objective = "reg:linear", eta = 0.01, gamma = 1, max_depth = 6, subsample = 0.8, colsample_bytree = 0.5 ) # Converting train and test into xgb.DMatrix format Dtrain = xgb.DMatrix( data = as.matrix(train[, -c("Item_Identifier", "Item_Outlet_Sales")]), label = train$Item_Outlet_Sales) Dtest = xgb.DMatrix( data = as.matrix(test[, -c("Item_Identifier")])) # 5-fold cross-validation to # find optimal value of nrounds set.seed(112) # Setting seed xgbcv = xgb.cv(params = param_list, data = Dtrain, nrounds = 1000, nfold = 5, print_every_n = 10, early_stopping_rounds = 30, maximize = F) # Training XGBoost model at nrounds = 428 xgb_model = xgb.train(data = Dtrain, params = param_list, nrounds = 428) xgb_model # Variable Importance var_imp = xgb.importance( feature_names = setdiff(names(train), c("Item_Identifier", "Item_Outlet_Sales")), model = xgb_model) # Importance plot xgb.plot.importance(var_imp) |
Output:
- Training of Xgboost model:
The xgboost model is trained calculating the train-rmse score and test-rmse score and finding its lowest value in many rounds.
- Model xgb_model:
The XgBoost models consist of 21 features with the objective of regression linear, eta is 0.01, gamma is 1, max_depth is 6, subsample is 0.8, colsample_bytree = 0.5, and silent is 1.
- Variable Importance plot:
The Item_MRP is the most important variable followed by Item_Visibility and Outlet_Location_Type_num.
These are the general steps to use XGBoost in R. Keep in mind that the specific details of our workflow will depend on your dataset and the problem you are trying to solve. XGBoost provides a tools that are powerful for building predictive model in R.
